Effectivenes, Safety, and Patient Preferences of Infliximab Biosimilar Medications for Inflammatory Bowel Disease

背景：生物药物（生物制剂）对免疫系统疾病，癌症， 和其他条件;然而，他们的高费用是护理的障碍和医疗保健系统的负担。 生物制剂不能完全复制为“通用”药物。生物仿制药-类似，但不相同的版本 生物药物-被批准具有巨大的潜在成本节约。然而，VA提供者和患者 关于生物类似药转换为疾病特异性随机对照药物的安全性和有效性的担忧 试验不需要批准。意义/影响：肿瘤坏死因子-α拮抗剂（抗TNF） 是生物仿制药中最大的一类生物制剂，其中转换可能可行以降低成本;但是 如何以患者可接受方式安全有效地整合它们的使用是未知的。这项建议 解决退伍军人安全的VA HSR优先事项，增加大量真实的- VA研究的世界影响，并使用卫生系统工程的交叉HSR方法，通过 学习医疗保健系统。创新：克罗恩病（CD）和溃疡性结肠炎（UC）是第1和第2位 VA中抗TNF的最常见适应症，可作为学习型医疗保健系统的模型 缓解生物类似药转换相关不良事件的方法。具体目标：目标1a：比较 继续接受抗TNF原药治疗的CD和UC患者与转换为 生物仿制药目的1b：比较继续接受抗TNF原药治疗的患者中CD或UC加重的发生率 转换为生物仿制药的患者。目的2：比较2a）传统回归的准确性和校准 模型vs. 2b）机器学习模型，用于预测与抗TNF相关的药物相关不良事件 使用CD和UC的VA用户。目标3：使用协商民主的方法来吸引退伍军人， 关于“喜欢”药物转换程序的偏好，无论他们是否知情， 围绕治疗方法达成共识。方法：通过回顾性研究实现目标1 2017-2019年国家VA数据集中接受抗TNF治疗的CD和UC患者队列研究。 将使用管理数据和手册相结合来确定不良事件和加重。 图表审查。目标1的分析将使用GEE通过泊松回归进行。调整后的事件发生率比 与继续使用原研生物类似药的患者相比，转换为生物类似药的患者将使用 95%置信区间和Wald p值将根据回归模型估计值得出。预测 发生抗TNF不良事件的患者将告知选择适当的治疗，并指导 患者进行生物类似药转换。对于目标2，传统的回归模型和机器学习模型 将构建以确定哪种模型更适合预测抗TNF相关不良事件。 通过比较这些模型来开发最好的风险分层工具将使我们能够识别退伍军人 有不良事件风险的机构，以提高退伍军人护理的质量和效率。至关 重要的是，退伍军人事务部的政策纳入了退伍军人对道德争议问题的意见， 他们的健康目标3将采用协商民主的方法，提供一个切实可行和可靠的方法， 在复杂的政策问题上征求知情和深思熟虑的意见。民主议事用教育 专家和同行之间精心组织的审议，以提供知情的意见和政策 有关利益攸关方的建议。后续步骤/实施：该提案得到以下方面的支持： 临床合作伙伴：国家VA炎症性肠病技术咨询小组和药房 受益管理人员，将通过VA特定生物类似药转换传播本研究的结果 临床指南和VA药房处方政策。未来的研究将包括实用的临床试验， 使用本提案中创建的学习医疗保健平台的其他生物类似药生物制剂。

Background: Biologic medications (biologics) are highly effective for diseases of the immune system, cancers, and other conditions; however, their high expense is a barrier to care and a burden to the healthcare system. Biologics cannot be exactly copied as “generic” medications. Biosimilars- similar, but not identical versions of biologic medications- are approved with large potential cost savings. However, VA providers and patients have concerns regarding biosimilar switching safety and effectiveness as disease-specific randomized controlled trials are not required for approval. Significance/Impact: Antagonists to tumor necrosis factor-α (Anti-TNFs) are the largest class of biologics with biosimilars where switching may be feasible to reduce costs; however how to safely and effectively integrate their use in a manner acceptable to patients is unknown. This proposal addresses the VA HSR priority of veteran safety, the ORD-wide research priority of increasing substantial real- world impact of VA research, and uses cross-cutting HSR methods of health systems engineering through a learning healthcare system. Innovation: Crohn’s disease (CD) and ulcerative colitis (UC) are the 1st and 2nd most common indications for Anti-TNFs in the VA and can serve as a model for a learning healthcare system approach for mitigation of adverse events related to biosimilar switching. Specific Aims: Aim 1a: To compare rates of adverse events in CD and UC patients continued on Anti-TNF originator to those switched to biosimilar. Aim 1b: To compare rates of CD or UC exacerbation in patients continued on Anti-TNF originator to those switched to biosimilar. Aim 2: To compare the accuracy and calibration of 2a) traditional regression models vs. 2b) machine learning models for predicting medication related adverse event related to Anti-TNF in VA users with CD and UC. Aim 3: To use deliberative democracy methods to engage Veterans, to elicit their preference regarding “like" medication switch programs with and without their knowledge and to develop consensus around treatment approaches. Methodology: Aim 1 will be achieved through a retrospective cohort study of CD and UC patients who received Anti-TNF from the national VA datasets from 2017-2019. Adverse events and exacerbations will be determined using a combination of administrative data and manual chart review. Analyses for Aim 1 will proceed by Poisson regression using GEE. Adjusted event rate ratios of patients switched to biosimilar compared to those who continued on originator biosimilar will be calculated with 95% confidence intervals and Wald p-values will be derived from the regression model estimates. Prediction of patients who have adverse events to Anti-TNF will inform selection of appropriate therapy, and guidance of patients for biosimilar switching. For Aim 2, both traditional regression models and machine learning models will be constructed to identify which model will be better for predicting Anti-TNF related adverse events. Developing the best possible risk stratification tool by comparing these models will allow us to identify veterans that are at risk of adverse events to improve both the quality and efficiency of veteran care. It is critically important that VA policies incorporate the opinions of Veterans on ethically controversial issues that impact their health. Aim 3 will employ deliberative democratic methods that offer a practical and reliable approach to soliciting informed and considered opinions in complex policy issues. Democratic deliberation uses education by experts and carefully structured deliberation among peers to deliver informed opinions and policy suggestions from concerned stakeholders. Next Steps/ Implementation: This proposal is supported with clinical partners: the national VA Inflammatory Bowel Disease Technical Advisory Group and Pharmacy Benefits Management who will disseminate findings from this study through VA-specific biosimilar switch clinical guidelines and VA-pharmacy prescription policy. Future studies will include pragmatic clinical trials of other biosimilar biologics using the learning healthcare platform created in this proposal.